NDLI: Inpainting color images in learned dictionary

Content Provider	IEEE Xplore Digital Library
Author	Filipovic, M. Kopriva, I. Cichocki, A.
Copyright Year	2012
Description	Author affiliation: RIKEN Brain Science Institute/Laboratory for Advanced Brain Signal Processing, Wako-shi, Saitama, 351-0198, Japan (Cichocki, A.) \|\| Ruđer Bošković Institute/Division of Laser and Atomic R&D, Bijenička cesta 54, 10002, Zagreb, Croatia (Filipovic, M.; Kopriva, I.)
Abstract	Sparse representation of natural images over redundant dictionary enables solution of the inpainting problem. A major challenge, in this regard, is learning of a dictionary that is well adapted to the image. Efficient methods are developed for grayscale images represented in patch space by using, for example, K-SVD or independent component analysis algorithms. Here, we address the problem of patch space-based dictionary learning for color images. To this end, an image in RGB color space is represented as a collection of vectorized 3D patch tensors. This leads to the state-of-the-art results in inpainting random and structured patterns of missing values as it is demonstrated in the paper.
Starting Page	66
Ending Page	70
File Size	4071329
Page Count	5
File Format	PDF
ISBN	9781467310680
ISSN	20761465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-27
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	EURASIP
Subject Keyword	Training Dictionaries Tensile stress Image color analysis Inpainting Color Independent component analysis Minimization Image reconstruction Color image Learned dictionary
Content Type	Text
Resource Type	Article

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